Best practices in plant fluorescence imaging and reporting: A primer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microscopy is a fundamental approach for plant cell and developmental biology as well as an essential tool for mechanistic studies in plant research. However, setting up a new microscopy-based experiment can be challenging, especially for beginner users, when implementing new imaging workflows or when working in an imaging facility where staff may not have extensive experience with plant samples. The basic principles of optics, chemistry, imaging, and data handling are shared among all cell types. However, unique challenges are faced when imaging plant specimens due to their waxy cuticles, strong/broad spectrum autofluorescence, recalcitrant cell walls, and air spaces that impede fixation or live imaging, impacting sample preparation and image quality. As expert plant microscopists, we share our collective experience on best practices to improve the quality of published microscopy results and promote transparency, reproducibility, and data reuse for meta-analyses. We offer plant-specific advice and examples for microscope users at all stages of fluorescence microscopy workflows, from experimental design through sample preparation, image acquisition, processing, and analyses, to image display and methods reporting in manuscripts. We also present standards for methods reporting that will be valuable to all users and offer tools to improve reproducibility and data sharing.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it